The predict() function in R is a tool used for making predictions from models By using predict(), we can generate predictions based on the fitted model and new input data. The predict() function in R is used to make predictions based on the model object we create. It can predict both the response variable (i.e., the dependent variable) for a new set of input variables or provide model diagnostics such as residuals, confidence intervals or fitted values.
Syntax
predict(object, newdata, type = "response", se.fit = FALSE, ...)
- object: The model object (e.g., the result of a lm(), glm() or rpart() function).
- newdata: The new data for which predictions are required.
- type: Specifies the type of prediction to return (e.g., response, link, etc.).
- se.fit: If TRUE, standard errors of the predicted values will be returned.
Here is how we can use predict() in different contexts:
Example 1: Predicting with a Linear Model
Let's start by fitting a simple linear regression model using the lm() function and then making predictions using predict().
R # Sample data set.seed(123) d <- data.frame( x = rnorm(100, mean = 10, sd = 5), y = rnorm(100, mean = 50, sd = 10) ) # Fit the linear model model <- lm(y ~ x, data = d) n_d <- data.frame(x = c(11, 12, 13)) predictions <- predict(model, newdata = n_d) print(predictions)
In this case, the predict() function will return the predicted values of y for the new x values (11, 12 and 13).
Output:
1 2 3
48.86703 48.76208 48.65714
Example 2: Predicting with a Logistic Regression Model
let's use logistic regression with the glm() function and make predictions using the predict() function.
R set.seed(123) d_logit <- data.frame( age = rnorm(100, mean = 30, sd = 5), bought = sample(0:1, 100, replace = TRUE) ) # Fit a logistic regression model model_logit <- glm(bought ~ age, data = d_logit, family = binomial) n_d_logit <- data.frame(age = c(28, 32, 35)) pre_logit <- predict(model_logit, newdata = n_d_logit, type = "response") print(pre_logit)
Here, the type = "response" argument tells predict() to return probabilities for the bought variable
Output:
1 2 3
0.4470355 0.5169052 0.5690249
Example 3: Predicting Class Labels with a Decision Tree
In this example, we will use the rpart() function to create a decision tree and predict class labels for new data.
R install.packages("rpart") library(rpart) data_iris <- iris # Fit a decision tree model model_tree <- rpart(Species ~ Sepal.Length + Sepal.Width, data = data_iris) new_data_tree <- data.frame(Sepal.Length = c(5.1, 6.3, 7.2), Sepal.Width = c(3.5, 3.3, 3.1)) predictions_tree <- predict(model_tree, newdata = new_data_tree, type = "class") print(predictions_tree) Here, the type = "class" argument returns the predicted class labels (setosa, versicolor or virginica).
Output:
1 2 3
setosa virginica virginica
Levels: setosa versicolor virginica
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